Allele Frequencies in World Populations

HLA > Haplotype Frequency Search

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A B C DRB1 DPA1 DPB1 DQA1 DQB1

Population:  Country:  Source of dataset : 
Region:  Ethnic Origin:     Type of study :  Sort by: 
Sample Size:      Sample Year:     Loci Tested: 
Displaying 1 to 100 (from 209) records   Pages: 1 2 3 of 3  

Line Haplotype Population Frequency (%) Sample Size Distribution¹
 1  A*74-B*57-DRB1*15-DQB1*06  Mexico Veracruz, Orizaba 1.666760
 2  A*30-B*57-DRB1*15-DQB1*06  Mexico Hidalgo Rural 1.234681
 3  A*03-B*57-DRB1*15-DQB1*06  Mexico Zacatecas, Zacatecas city 1.190584
 4  A*36-B*57-DRB1*15-DQB1*06  Mexico Campeche Rural 1.063847
 5  A*01-B*57-C*06-DRB1*15-DQB1*06  Sudan Khartoum 1.020098
 6  A*02-B*57-DRB1*15-DQB1*06  Mexico Mexico City Metropolitan Area Rural 0.9868150
 7  A*02-B*57-DRB1*15-DQB1*06  Mexico Guanajuato Rural 0.9202162
 8  A*02-B*57-DRB1*15-DQA1*01:03-DQB1*06:01  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.9100112
 9  A*01-B*57-C*07-DRB1*15-DQA1*01-DQB1*06  Mexico Tapachula, Chiapas Mestizo Population 0.694472
 10  B*57-C*07-DRB1*15-DQA1*01-DQB1*06  Mexico Tapachula, Chiapas Mestizo Population 0.694472
 11  A*01-B*57-DRB1*15-DQB1*06  Mexico Coahuila Rural 0.6881216
 12  A*02:05-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02-DPB1*02:01  Tanzania Maasai 0.6390336
 13  A*02-B*57-C*03-DRB1*15-DQB1*06  Sudan Khartoum 0.510098
 14  A*02-B*57-C*06-DRB1*15-DQB1*06  Sudan Khartoum 0.510098
 15  A*11-B*57-DRB1*15-DQA1*01:03-DQB1*06:01  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.4400112
 16  A*24-B*57-DRB1*15-DQA1*01:03-DQB1*06:01  Russia, South Ural, Chelyabinsk region, Nagaybaks 0.4400112
 17  A*24-B*57-DRB1*15-DQB1*06  Mexico Michoacan Rural 0.4298348
 18  A*02-B*57-DRB1*15-DQB1*06  Mexico Chiapas Rural 0.4132121
 19  A*30:02:01-B*57:03:01-C*18:02-DRB1*15:03:01-DQB1*06:02:01-DPB1*01:01:01  South African Black 0.3520142
 20  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.3378296
 21  A*32-B*57-DRB1*15-DQB1*06  Mexico Mexico City Center 0.3247152
 22  A*01-B*57-DRB1*15-DQB1*06  Mexico Durango, Durango city 0.3226153
 23  A*01:01-B*57:03-C*16:01-DRB1*15:03-DQB1*06:02-DPB1*02:01  Tanzania Maasai 0.3195336
 24  A*01-B*57-DRB1*15-DQB1*06  Mexico Durango Rural 0.3058326
 25  A*11-B*57-DRB1*15-DQB1*06  Mexico Jalisco, Zapopan 0.2976168
 26  A*01:01:01-B*57:01:01-C*06:02:01-DRB1*15:02:02-DQB1*06:01:01  India Karnataka Kannada Speaking 0.2870174
 27  A*31:01:02-B*57:76-C*04:01:01-DRB1*15:01:01-DQB1*06:02:01  India Karnataka Kannada Speaking 0.2870174
 28  A*36-B*57-DRB1*15-DQB1*06  Mexico Sinaloa Rural 0.2732183
 29  A*24:02:01-B*57:01:01-C*12:02:02-DRB1*15:02:02-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 30  A*24:02:01-B*57:01:01-C*12:03:01-DRB1*15:01:01-DQB1*06:01:01  India Andhra Pradesh Telugu Speaking 0.2688186
 31  A*02:01-B*57:03-C*16:01-DRB1*15:03-DQA1*01:02-DQB1*06:02-DPB1*18:01  USA San Diego 0.2600496
 32  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India East UCBB 0.24652,403
 33  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India Tamil Nadu 0.22552,492
 34  A*01:01-B*57:01-C*07:01-DRB1*15:01-DQB1*06:02-DPB1*04:01  Panama 0.1900462
 35  A*30:02-B*57:03-C*03:05-DRB1*15:03-DQB1*06:02-DPB1*13:01  Panama 0.1900462
 36  A*03-B*57-DRB1*15-DQB1*06  Mexico Zacatecas Rural 0.1859266
 37  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  Malaysia Peninsular Indian 0.1845271
 38  A*24:02-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  Malaysia Peninsular Indian 0.1845271
 39  A*02:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02  Germany DKMS - Italy minority 0.17301,159
 40  A*02:11-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Northeast UCBB 0.1689296
 41  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India North UCBB 0.16315,849
 42  A*01:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02-DPB1*55:01  Tanzania Maasai 0.1597336
 43  A*01:09-B*57:02-C*07:76-DRB1*15:03-DQB1*06:02-DPB1*04:01  Tanzania Maasai 0.1597336
 44  A*02:02-B*57:03-C*07:371-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 45  A*30:01-B*57:03-C*01:35-DRB1*15:03-DQB1*06:02-DPB1*15:01  Tanzania Maasai 0.1597336
 46  A*01-B*57-DRB1*15:01-DQA1*01:02-DQB1*06:02  Brazil Paraná Caucasian 0.1560641
 47  A*02:01-B*57:02-DRB1*15:01-DQB1*06:03  Mexico Mexico City Tlalpan 0.1515330
 48  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India West UCBB 0.14265,829
 49  A*33:03-B*57:01-C*06:02-DRB1*15:01-DQA1*01:02-DQB1*06:01-DPB1*04:01  Sri Lanka Colombo 0.1401714
 50  A*01:01:01-B*57:01:01-C*06:02:01-DRB1*15:01:01-DQB1*06:01:01  India Kerala Malayalam speaking 0.1400356
 51  A*31:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02  Italy pop 5 0.1400975
 52  A*01:01:01:01-B*57:01:01-C*06:02:01:01-DRB1*15:01:01-DQB1*06:02:01  Russia Nizhny Novgorod, Russians 0.13481,510
 53  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India South UCBB 0.133411,446
 54  A*01:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.13302,411
 55  A*68-B*57-DRB1*15-DQB1*06  Mexico Mexico City North 0.1328751
 56  A*30:02-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.13102,411
 57  A*30:02-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA NMDP Black South or Central American 0.12664,889
 58  A*03:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.12102,411
 59  A*02-B*57-DRB1*15-DQB1*06  Mexico Puebla Rural 0.1199833
 60  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India South UCBB 0.099711,446
 61  A*01-B*57-DRB1*15-DQB1*06  Mexico Veracruz Rural 0.0924539
 62  A*30:02-B*57:02-C*18:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.08702,411
 63  A*02-B*57-DRB1*15:03-DQA1*01:02-DQB1*06:02  Brazil Paraná Caucasian 0.0780641
 64  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India West UCBB 0.07445,829
 65  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQA1*01:03-DQB1*06:01-DPB1*13:01  Sri Lanka Colombo 0.0700714
 66  A*02:11-B*57:01-C*06:02-DRB1*15:02-DQA1*01:03-DQB1*06:01-DPB1*14:01  Sri Lanka Colombo 0.0700714
 67  A*24:02-B*57:01-C*07:27-DRB1*15:01-DQA1*01:02-DQB1*06:02-DPB1*04:01  Sri Lanka Colombo 0.0700714
 68  A*26:01-B*57:01-C*06:02-DRB1*15:02-DQA1*01:03-DQB1*06:01-DPB1*26:01  Sri Lanka Colombo 0.0700714
 69  A*01:01-B*57:01-C*07:01-DRB1*15:01-DQB1*06:02  Colombia Bogotá Cord Blood 0.06841,463
 70  A*01:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  Colombia Bogotá Cord Blood 0.06841,463
 71  A*01:01-B*57:01-C*07:01-DRB1*15:01-DQB1*06:02  Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, 0.06804,335
 72  A*02-B*57-DRB1*15-DQB1*06  Mexico Mexico City North 0.0664751
 73  A*02-B*57-C*07-DRB1*15-DQA1*01-DQB1*06  Spain, Castilla y Leon, Northwest, 0.06571,743
 74  A*11:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India West UCBB 0.06405,829
 75  A*02-B*57-DRB1*15-DQB1*06  Mexico Tlaxcala Rural 0.0602830
 76  A*24:02-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India East UCBB 0.05992,403
 77  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India Central UCBB 0.05984,204
 78  A*01:01:01-B*57:01:01-C*06:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.059323,595
 79  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02-DPB1*04:01  Russia Karelia 0.05821,075
 80  A*33:03-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India Central UCBB 0.05534,204
 81  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02-DPB1*04:01  Germany DKMS - German donors 0.05313,456,066
 82  A*32:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India West UCBB 0.05225,829
 83  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Central UCBB 0.05054,204
 84  A*30-B*57-DRB1*15-DQB1*06  Mexico Puebla, Puebla city 0.05011,994
 85  A*01:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.04752,492
 86  A*32:01-B*57:03-C*02:02-DRB1*15:03-DQB1*06:02  USA Hispanic pop 2 0.04701,999
 87  A*68:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India East UCBB 0.04462,403
 88  A*02:02-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.04402,411
 89  A*30:01-B*57:03-C*18:01-DRB1*15:01-DQB1*06:02  USA African American pop 4 0.04402,411
 90  A*68:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.04402,411
 91  A*74:01-B*57:03-C*07:01-DRB1*15:03-DQB1*06:02  USA African American pop 4 0.04402,411
 92  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02  Germany DKMS - Italy minority 0.04301,159
 93  A*02:01-B*57:03-C*07:01-DRB1*15:01-DQB1*06:02  Germany DKMS - Italy minority 0.04301,159
 94  A*01-B*57-DRB1*15-DQB1*06  Mexico Jalisco, Guadalajara city 0.04191,189
 95  A*24:11N-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India East UCBB 0.04162,403
 96  A*02:01:01-B*57:01:01-C*06:02:01-DRB1*15:01:01-DQB1*06:02:01  Poland BMR 0.041223,595
 97  A*02:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.04092,492
 98  A*68:01-B*57:01-C*06:02-DRB1*15:02-DQB1*06:01  India Tamil Nadu 0.04012,492
 99  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:02  India North UCBB 0.03865,849
 100  A*01:01-B*57:01-C*06:02-DRB1*15:01-DQB1*06:01  India North UCBB 0.03585,849

Notes:

* Haplotype Frequencies: Total number of copies of the haplotype in the population sample (Haplotypes / 2n) shown in percentages (%).
   Important: This field has been expanded to two decimals to better represent frequencies of large datasets (e.g. where sample size > 1000 individuals)
¹ Distribution - Shows the geographic distribution in overlaid maps of the complete haplotype (left icon) or the input alleles if low level resolution was entered (right icon).


Displaying 1 to 100 (from 209) records   Pages: 1 2 3 of 3  


   

Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools
Gonzalez-Galarza FF, McCabe A, Santos EJ, Jones J, Takeshita LY, Ortega-Rivera ND, Del Cid-Pavon GM, Ramsbottom K, Ghattaoraya GS, Alfirevic A, Middleton D and Jones AR Nucleic Acid Research 2020, 48:D783-8.
Liverpool, U.K.

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